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  • Title: Optimizing the acquisition time of the middle latency response using maximum length sequences and chirps.
    Author: Bell SL, Allen R, Lutman ME.
    Journal: J Acoust Soc Am; 2002 Nov; 112(5 Pt 1):2065-73. PubMed ID: 12430818.
    Abstract:
    The middle latency response (MLR) may be used as an indicator of anesthetic depth but has been criticized due to its long acquisition time. This study explores methods for optimizing recording of the MLR to maximize signal-to-noise ratio (SNR) and hence reduce acquisition time. The first experiment investigates the effects of increasing stimulation rate beyond conventional values and also using higher rates by means of maximum length sequences (MLS). The second experiment compares the use of click and chirp stimuli to elicit the MLR, both at conventional and MLS stimulation rates. For all conditions total recording duration is fixed at 185 s and stimulation level is fixed at 60 dB SL. It was found that SNR increases progressively with rate using conventional click stimulation until the theoretical rate limit is reached at the reciprocal of the response duration. The SNR improvement is equivalent to increasing test speed by a factor of 3. Using MLS stimulation, the SNR increases further until a maximum is reached at a rate of 167 clicks/s, equivalent to a fivefold test speed improvement relative to a conventional recording at 5 clicks/s. The use of chirp stimuli designed to compensate for the frequency dependent cochlear traveling wave delay produces an increase in wave V-Na amplitude at all recording rates. For the later latency waves of the response an increase in amplitude is seen for MLS, but not for conventional chirp trains. The optimum SNR was obtained using chirp stimuli at a MLS rate of 167 opportunities/s. It is concluded that the combination of chirps and MLS can reduce acquisition time to less than one-tenth of that required for conventional stimulation at 5 clicks/s for the same SNR. This would confer material benefits for estimating anesthetic depth using MLR.
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